Prediction model for recruitment of railway bureaus and enrolment of railway schools based on deep learning

With urbanisation accelerating, the demand for railway transportation is increasing, making it essential to plan recruitment for railway bureaus and adjust enrolment at railway schools. This study aims to accurately predict recruitment needs using historical data. We applied deep learning models, including back propagation neural network (BP neural network), long short-term memory (LSTM), and LSTM-attention, to forecast recruitment numbers for eight positions across 18 railway bureaus in 2025, yielding MAE values of 100,000, 0.16, and 0.13, respectively. We also used linear regression, ridge regression, LASSO regression, and random forests to predict the number of remaining graduates in eight major railway programs for 2025, with most models showing MSE values between 0 and 4. Finally, we established upper and lower limits for vocational student enrolment quotas in 2025 by applying factors of 80% and 75% to the predicted recruitment numbers. These findings provide valuable insights for recruitment and enrolment planning, enhancing the application of deep learning in railway recruitment forecasting.